mirror of
https://github.com/hwchase17/langchain
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1b3942ba74
Description: Adding NIBittensorLLM via Validator Endpoint to langchain
llms
Tag maintainer: @Kunj-2206
Maintainer responsibilities:
Models / Prompts: @hwchase17, @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
38 lines
975 B
Plaintext
38 lines
975 B
Plaintext
# NIBittensor
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This page covers how to use the BittensorLLM inference runtime within LangChain.
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It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.
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## Installation and Setup
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- Install the Python package with `pip install langchain`
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## Wrappers
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### LLM
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There exists a NIBittensor LLM wrapper, which you can access with:
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```python
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from langchain.llms import NIBittensorLLM
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```
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It provides a unified interface for all models:
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```python
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llm = NIBittensorLLM(system_prompt="Your task is to provide consice and accurate response based on user prompt")
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print(llm('Write a fibonacci function in python with golder ratio'))
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```
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Multiple responses from top miners can be accessible using the `top_responses` parameter:
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```python
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multi_response_llm = NIBittensorLLM(top_responses=10)
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multi_resp = multi_response_llm("What is Neural Network Feeding Mechanism?")
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json_multi_resp = json.loads(multi_resp)
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print(json_multi_resp)
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```
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